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@ARTICLE{Rudolph:132915,
      author       = {A. Rudolph$^*$ and M. Song and M. N. Brook and R. L. Milne
                      and N. Mavaddat and K. Michailidou and M. K. Bolla and Q.
                      Wang and J. Dennis and A. N. Wilcox and J. L. Hopper and M.
                      C. Southey and R. Keeman and P. A. Fasching and M. W.
                      Beckmann and M. Gago-Dominguez and J. E. Castelao and P.
                      Guénel and T. Truong and S. E. Bojesen and H. Flyger and H.
                      Brenner$^*$ and V. Arndt$^*$ and H. Brauch$^*$ and T.
                      Brüning and A. Mannermaa and V.-M. Kosma and D. Lambrechts
                      and M. Keupers and F. J. Couch and C. Vachon and G. G. Giles
                      and R. J. MacInnis and J. Figueroa and L. Brinton and K.
                      Czene and J. S. Brand and M. Gabrielson and K. Humphreys and
                      A. Cox and S. S. Cross and A. M. Dunning and N. Orr and A.
                      Swerdlow and P. Hall and P. D. P. Pharoah and M. K. Schmidt
                      and D. F. Easton and N. Chatterjee and J. Chang-Claude$^*$
                      and M. García-Closas},
      title        = {{J}oint associations of a polygenic risk score and
                      environmental risk factors for breast cancer in the {B}reast
                      {C}ancer {A}ssociation {C}onsortium.},
      journal      = {International journal of epidemiology},
      volume       = {47},
      number       = {2},
      issn         = {1464-3685},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {DKFZ-2018-00557},
      pages        = {526 - 536},
      year         = {2018},
      abstract     = {Polygenic risk scores (PRS) for breast cancer can be used
                      to stratify the population into groups at substantially
                      different levels of risk. Combining PRS and environmental
                      risk factors will improve risk prediction; however,
                      integrating PRS into risk prediction models requires
                      evaluation of their joint association with known
                      environmental risk factors.Analyses were based on data from
                      20 studies; datasets analysed ranged from 3453 to 23 104
                      invasive breast cancer cases and similar numbers of
                      controls, depending on the analysed environmental risk
                      factor. We evaluated joint associations of a 77-single
                      nucleotide polymorphism (SNP) PRS with reproductive history,
                      alcohol consumption, menopausal hormone therapy (MHT),
                      height and body mass index (BMI). We tested the null
                      hypothesis of multiplicative joint associations for PRS and
                      each of the environmental factors, and performed global and
                      tail-based goodness-of-fit tests in logistic regression
                      models. The outcomes were breast cancer overall and by
                      estrogen receptor (ER) status.The strongest evidence for a
                      non-multiplicative joint associations with the 77-SNP PRS
                      was for alcohol consumption (P-interaction = 0.009),
                      adult height (P-interaction = 0.025) and current use of
                      combined MHT (P-interaction = 0.038) in ER-positive
                      disease. Risk associations for these factors by percentiles
                      of PRS did not follow a clear dose-response. In addition,
                      global and tail-based goodness of fit tests showed little
                      evidence for departures from a multiplicative risk model,
                      with alcohol consumption showing the strongest evidence for
                      ER-positive disease (P = 0.013 for global and 0.18 for
                      tail-based tests).The combined effects of the 77-SNP PRS and
                      environmental risk factors for breast cancer are generally
                      well described by a multiplicative model. Larger studies are
                      required to confirm possible departures from the
                      multiplicative model for individual risk factors, and assess
                      models specific for ER-negative disease.},
      cin          = {C020 / C070 / G110 / L101 / L801},
      ddc          = {610},
      cid          = {I:(DE-He78)C020-20160331 / I:(DE-He78)C070-20160331 /
                      I:(DE-He78)G110-20160331 / I:(DE-He78)L101-20160331 /
                      I:(DE-He78)L801-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29315403},
      pmc          = {pmc:PMC5913605},
      doi          = {10.1093/ije/dyx242},
      url          = {https://inrepo02.dkfz.de/record/132915},
}